Cancer patient stratification biomarkers
By assessing gut microbiota taxa in stool samples, the method accurately stratifies cancer patients into responders and non-responders to ICI treatments, reducing costs and side effects while enhancing treatment efficacy.
WO2026176117A1 Publication Date: 2026-08-27BLUMAIDEN BIO PTE LTD +1
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Patent Information
- Application Number
- PCT/EP2026/054956
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- Priority Date
- 2025-02-24
- Filing Date
- 2026-02-24
- Publication Date
- 2026-08-27
AI Technical Summary
Technical Problem
Current biomarkers for predicting cancer patients' responsiveness to Immune Checkpoint Inhibitor (ICI) treatments are invasive, have limited predictive power, or are costly, leading to unnecessary treatments and severe side effects.
Method used
A method using the relative abundance of specific gut microbiota taxa, such as Acidaminococcaceae, Pasteurellaceae, and others, assessed in stool samples, to stratify patients into responders and non-responders through a non-linear model, reducing the need for invasive biopsies and costly treatments.
Benefits of technology
This method effectively stratifies patients, reducing healthcare costs and minimizing severe side effects by ensuring only responders receive ICI treatments, improving treatment efficacy and patient outcomes.
✦ Generated by Eureka AI based on patent content.
Abstract
The present invention relates to a method of predicting the responsiveness to Immune Checkpoint Inhibitor (ICI)-based treatments in cancer subjects at baseline based on the relative abundances of at least 2 of 8 microbial taxa in a stool sample from the patient. The method produces a binary classification of patients as responders and non-responders to ICI-based treatments. More particularly, the stratification method relates to stratifying hepatocellular carcinoma patients into 2 classes. The invention also relates to ICI-based treatments that can be administered to responders. While the identified biomarkers partially overlap the previously reported ones, the novelty of this invention is based on a) a combination of mixed-level taxonomy (combining genus- and family-level taxonomic information) and b) development of an original machine learning pipeline to leverage this type of input. Also provided is a method of normalisation and look-up table for carrying out the invention on arbitrary datasets, shown to exactly reproduce results from a deep learning model. As a result, while the markers are partially known, their context of use such as combination with markers of different nature and in-house machine learning architecture resulted in the predictive performance that was never seen before for this task and data type.
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